Hi Terry, Greg, and Marc, Thanks for your advice about this. I think I have a pretty good starting point now for the analysis. Appreciate your help. Paul
--- On Wed, 7/18/12, Terry Therneau <[email protected]> wrote: From: Terry Therneau <[email protected]> Subject: Re: [R] Power analysis for Cox regression with a time-varying covariate To: "Marc Schwartz" <[email protected]>, "Greg Snow" <[email protected]>, [email protected], "Paul Miller" <[email protected]> Received: Wednesday, July 18, 2012, 8:24 AM Marc gave the referencer for Schoenfeld's article. It's actually quite simple. Sample size for a Cox model has two parts: 1. Easy part: how many deaths to I need d = (za + zb)^2 / [var(x) * coef^2] za = cutoff for your alpah, usually 1.96 (.05 two-sided) zb = cutoff for power, often 0.84 = qnorm(.8) = 80% power var(x) = variance of the covariate you are testing. For a yes/no variable like treatment this would be p(1-p) where p = fraction on the first arm coef = the target coefficient in your Cox model. For an "increase in survival of 50%" we need exp(coef)=1.5 or coef=.405 All leading to the value I've memorized by now of (1.96 + 0.84)^2 /(.25* .405^2) = 191 deaths for a balanced two arm study to detect a 50% increase in survival. 2. Hard part: How many patients will I need to recruit, over what interval of time, and with how much total follow-up to achieve this number of events? I never use the canned procedures for sample size because this second part is so study specific. And frankly, it's always a guesstimate. Death rates for a condidtion will usually drop by 1/3 as soon as you start enrolling subjects. Terry T. [[alternative HTML version deleted]]
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